Convergence of Solutions and Practical Stability of Hopfield-type Neural Networks with Time-Varying External Inputs

Abstract

Via the direct method of Lyapunov, this paper presents a convergence criterion for Hopfield-type artificial neural networks with time-varying external inputs. Also, in the presence of such inputs, it is shown, via the concept of practical stability, that the boundedness of the neuron activation functions is all that is required to ensure boundedness of solutions